Analysis of omega-3 and omega-6 polyunsaturated fatty acid metabolism by compound-specific isotope analysis in humans
Bibliographic record
Abstract
2. Abstract Natural variations in the 13 C: 12 C ratio (carbon-13 isotopic abundance [δ 13 C]) of the food supply have been used to determine the dietary origin and metabolism of fatty acids, especially in the n-3 polyunsaturated fatty acid (PUFA) biosynthesis pathway. However, n-6 PUFA metabolism following linoleic acid (LNA) intake remains under investigated. Here, we sought to use natural variations in the δ 13 C signature of dietary oils and fatty fish to analyze n-3 and n-6 PUFA metabolism following dietary changes in LNA and eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) in adult humans. Participants with migraine (aged 38.6 ± 2.3 y, 93% female, BMI 27.0 ± 1.1 kg/m 2 ) were randomly assigned to one of three dietary groups for 16 weeks: 1) low omega-3, high omega-6 (H6), 2) high omega-3, high omega-6 (H3H6), or 3) high omega-3, low omega-6 (H3). Blood was collected at baseline, 4, 10, and 16 weeks. Plasma PUFA concentrations and δ 13 C were determined. The H6 intervention exhibited increases in plasma LNA δ 13 C signature over time; meanwhile, plasma LNA concentrations were unchanged. No changes in plasma arachidonic acid δ 13 C or concentration were observed. Participants on the H3H6 and H3 interventions demonstrated increases in plasma EPA and DHA concentration over time. Plasma δ 13 C-EPA increased in total lipids of the H3 group and phospholipids of the H3H6 group compared to baseline. Compound-specific isotope analysis supports a tracer-free technique that can track metabolism of dietary fatty acids in humans, provided that the isotopic signature of the dietary source is sufficiently different from plasma δ 13 C.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".